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Overview

James Theiler is a researcher affiliated with Los Alamos National Laboratory in the United States. Their primary field of study is Medicine, with a particular focus on Infectious Diseases. Theiler's research spans several related subfields, including Molecular Biology, Animal Science and Zoology, Geophysics, and Media Technology.

Theiler's work predominantly explores topics related to SARS-CoV-2 and COVID-19, with significant contributions to understanding virus detection, clinical research, and epidemiological studies. They have also conducted research on animal virus infection studies and remote-sensing image classification, as well as seismic waves and analysis.

The frequent publication venues for Theiler's research include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Cell Host & Microbe
  • IEEE Signal Processing Magazine
  • Cell

Theiler's collaborations feature several coauthors working closely on related scientific topics. These frequent coauthors include:

  • Bette Korber
  • Will Fischer
  • Hyejin Yoon
  • Kshitij Wagh
  • Brian Foley

Their recent research papers demonstrate a focus on viral mutations, transmissibility, and neutralization:

  • Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus, 2020, Cell
  • Spike mutation pipeline reveals the emergence of a more transmissible form of SARS-CoV-2, 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing antibodies elicited by ancestral spike vaccines, 2021, Cell Host & Microbe
  • Multiple lineages of monkeypox virus detected in the United States, 2021-2022, 2022, Science
  • Substantial Neutralization Escape by SARS-CoV-2 Omicron Variants BQ.1.1 and XBB.1, 2023, New England Journal of Medicine

Best Publications

  • Testing for nonlinearity in time series: the method of surrogate data

    James Theiler;Stephen Eubank;André Longtin;Bryan Galdrikian

  • Estimating fractal dimension

    James Theiler

  • Spurious dimension from correlation algorithms applied to limited time-series data

    James Theiler

  • Generating Surrogate Data for Time Series with Several Simultaneously Measured Variables

    Dean Prichard;James Theiler

  • Accelerated search for materials with targeted properties by adaptive design.

    Dezhen Xue;Dezhen Xue;Prasanna V. Balachandran;John Hogden;James Theiler

  • Accurate on-line support vector regression

    Junshui Ma;James Theiler;Simon Perkins

  • Grafting: fast, incremental feature selection by gradient descent in function space

    Simon Perkins;Kevin Lacker;James Theiler

  • Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram.

    James Theiler;James Theiler;Paul E. Rapp

  • Efficient algorithm for estimating the correlation dimension from a set of discrete points

    James Theiler

  • Constrained-realization Monte-Carlo method for hypothesis testing

    James Theiler;James Theiler;Dean Prichard;Dean Prichard;Dean Prichard

  • Online feature selection using grafting

    Simon Perkins;James Theiler

  • Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery

    C.C. Funk;J. Theiler;D.A. Roberts;C.C. Borel

  • Adaptive Strategies for Materials Design using Uncertainties.

    Prasanna V. Balachandran;Dezhen Xue;Dezhen Xue;James Theiler;John Hogden

  • Using Surrogate Data to Detect Nonlinearity in Time Series

    J. Theiler;B. Galdrikian;A. Longtin;S. Eubank

  • Generalized redundancies for time series analysis

    Dean Prichard;Dean Prichard;James Theiler

  • Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware

    Mike Estlick;Miriam Leeser;James Theiler;John J. Szymanski

  • An Overview of Background Modeling for Detection of Targets and Anomalies in Hyperspectral Remotely Sensed Imagery

    Stefania Matteoli;Marco Diani;James Theiler

  • Don't Bleach Chaotic Data

    James Theiler;Stephen Eubank

  • Genetic algorithms and support vector machines for time series classification

    Damian R. Eads;Daniel Hill;Sean Davis;Simon J. Perkins

  • Statistical precision of dimension estimators.

    James Theiler;James Theiler

  • Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

    N.R. Harvey;J. Theiler;S.P. Brumby;S. Perkins

  • Nonlinear modeling of chaotic time series: Theory and applications

    M. Casdagli;S. Eubank;J.D. Farmer;J. Gibson

Frequent Co-Authors

Bette T. Korber
Bette T. Korber Los Alamos National Laboratory
Brendt Wohlberg
Brendt Wohlberg Los Alamos National Laboratory
Maya Gokhale
Maya Gokhale Lawrence Livermore National Laboratory
Anthony B. Davis
Anthony B. Davis California Institute of Technology
Barton F. Haynes
Barton F. Haynes Duke University
Beatrice H. Hahn
Beatrice H. Hahn University of Pennsylvania
Dan H. Barouch
Dan H. Barouch Harvard Medical School
David C. Montefiori
David C. Montefiori Duke University
Norman L. Letvin
Norman L. Letvin Beth Israel Deaconess Medical Center
Roger Paredes
Roger Paredes University of Vic - Central University of Catalonia

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